Approaches to Carbon Emission Reductions and Technology in China’s Chemical Industry to Achieve Carbon Neutralization

Abstract

Based on China’s goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry.

1. Introduction and Literature Review

The chemical industry will remain an important part of China’s energy industry in terms of energy security, but a new chemical production path will help the industry achieve full decarbonization. The synthesis of hydrogen, carbon monoxide, and carbon dioxide as raw materials can create many major products in the chemical industry value chain. The biomass-based production path uses biomass as raw material and zero-carbon energy as process energy, including anaerobic digestion and gasification. Researchers have stated that “Climate change bears on China’s overall economic and social development and is crucial to China’s economic security, energy security, ecological security, food security, and the safety of people’s lives and property” [1].

Achieving this goal will be difficult, as China currently still has a coal and high-carbon energy structure. Although overall coal use has declined over the past decade, it still accounts for 57% of primary energy. Changing this situation must be approached one step at a time.

With the increasing attention being paid to environmental protection, carbon emissions have gradually become the focus of academic research. Existing studies have mainly focused on four aspects as follows.

The first aspect is carbon emission measurement and research. Most existing studies utilized the energy coefficient method given in the 2006 IPCC Guidelines for National GREENHOUSE Gas Inventories or the 2011 Guidelines for Provincial Greenhouse Gas Inventories. The difference between the two is mainly reflected in the difference between various energy default emission factors and that of time and space. Zhao [2] used the method provided by IPCC to calculate energy consumption and carbon emissions in Shanghai for 1994–2007 and found that the carbon emissions from energy consumption in Shanghai increased and the carbon emission intensity decreased. The second aspect is the study of factors influencing carbon emissions. Models created to study these factors include the Kaya Model [3], the LMDt model [4], and the STtRPAT model [5]. Among them, the Kaya model is mainly used to analyze the total carbon emissions of a country, whereas the LMDt model, which is based on the Kaya model, eliminates residual items and can be decomposed by addition and multiplication. The third aspect is the impact of carbon emissions on economic growth and finance. Wang and Zhen studied data for 2000–2015 to explore the relationship between carbon emissions and economic growth in the Yangtze River Economic Belt based on the level and mode of economic growth and established an environmental Kuznets curve for the Yangtze River Economic Belt, which showed that industrial structure and technological level had a restraining effect on carbon emissions; in addition, it predicted that the region could reach its carbon peak by 2030. Zeng [6] discussed the impact of carbon peak and carbon neutrality on finance from a national strategy perspective, suggesting that the realization of carbon neutrality would profoundly impact China’s energy, industry, and investment structure. The fourth aspect is carbon emission reduction research. Related scholars usually study the potential and paths of carbon emission reduction from an industry perspective. Wang [7] calculated the carbon emission reduction potential of China’s transportation by setting four scenarios—baseline, structural optimization, technological progress, and low-carbon—and found that the low-carbon scenario had the largest carbon emission reduction potential. Liu built an energy system model to predict the industry’s development trend. They explored possible paths of medium- and long-term low-carbon transition under different scenarios [8].

In summary, both domestic and foreign scholars have conducted many studies on carbon emission measurement, the factors influencing carbon emissions, the impact of carbon emissions on economic growth and finance, and carbon emission reduction paths. On 22 September 2020, in his address to the 75th Session of the United Nations General Assembly, President Xi Jinping said, “China will increase its nationally determined contribution and adopt more effective policies and measures to peak CO₂ emissions by 2030 and achieve carbon neutrality by 2060”. There have been few studies on carbon emission measurement, carbon emission’s influencing factors, and carbon emission reduction paths in the chemical industry to achieve carbon neutralization. Therefore, for this study, the carbon emissions of China’s chemical industry for 2010–2019 were calculated using the improved energy coefficient method. Then, the LMDt model was utilized to study the effects of emission structure, emission intensity, industrial structure, economic development level, and labor force scale on carbon emissions from the chemical industry. Finally, different scenarios were set to discuss the paths of carbon emission reductions in the chemical industry, and the carbon emission potential and peak of the chemical industry in the process of achieving carbon neutrality in China were studied.

2. Research Scope and Methods

2.1. Research Scope

The chemical industry mainly produces clean energy and alternative petroleum products. The main products are coal Alcohol to Olefin, alcohol to Alcohol to Olefin, coal to ethylene glycol, coal to natural gas, and coal to oil (both direct and indirect liquefaction), all six of which have been studied. According to the industrial chain, China’s chemical industry can be divided into upstream, middle, and downstream. The upstream of the industrial chain includes the production of raw materials, fuel, electricity, and steam, the middle of the industrial chain is the product production process, and the downstream is the extended processing of chemical products. Gao [9] showed that carbon emissions are the main contributory force in the upstream of the chemical industry chain, while the downstream extension of the chemical industry chain is based on other subsectors of deep processing manufacturing. Therefore, the system boundaries studied in this paper were the chemical production process and the upstream power and steam production process. Among them, the carbon emissions in the process of producing chemical products include emissions that occur when converting raw coal into chemical products and those from the direct combustion of fuel coal [2].

2.2. Research Methods

2.2.1. Method of Calculating the Carbon Emissions of the Chemical Industry

The carbon emissions of the chemical industry were calculated using the carbon emission coefficient method. All chemical industry raw materials and combustion are coal, but the difference between the types of coal usage is large, so it was difficult to use the energy carbon emission coefficient method to calculate carbon emissions. Thus, the carbon emissions in the chemical industry were calculated by multiplying the output of the final products by the carbon emission coefficient of the product production, as shown in Equation (1): [3]

$$C_{total}={\sum }_{t=1}^6{C}_t={\sum }_{t=1}^6{E}_t{F}_t$$

(1)

where C_total represents the total carbon emissions of the chemical industry (in millions of tons); t represents chemical products, of which there were six in total; C_t represents the carbon emissions of type T chemical product (in millions of tons); E_t represents the output of chemical product t (in millions of tons); F_t represents the carbon emission coefficient (tCO₂/t) of the chemical product t. The calculation methodology for CO₂ emissions depends exclusively on the F_t factor, that is, F_t represents the carbon emission coefficient (tCO₂/t) of the t chemical product. Zhang (2019), cited in the present manuscript, presented some mean values of F_t, based on analyzed enterprises (AW), and some previously reported literature values.

2.2.2. Decomposition Model of the Influential Factors of Chemical Industry Carbon Emissions

Combined with the calculation method of chemical industry carbon emissions, the LMDt model was used to decompose the influencing factors of carbon emissions into emissions structure, emission intensity, industrial structure, level of economic development, and size of the labor force, as depicted in Equation (2) [3]:

$$C_{total}={\sum }_{t=1}^6{C}_t={\sum }_{t=1}^6\frac{C_t}{C_{total}}\times \frac{C_{total}}{IV}\times \frac{IV}{G}\times \frac{G}{P}\times P$$

(2)

where IV represents the output value of the chemical industry (10 billion yuan); G represents the total industrial output value (10 billion yuan); P represents the average annual number of people employed in industry (in millions). Next, because

$$E{S}_t=\frac{C_t}{C_{total}}, Et=\frac{C_{total}}{IV}, tS=\frac{IV}{G}, EDL=\frac{G}{P}, LS=P$$

then

$$C_{total}={\sum }_{t=1}^6{C}_t={\sum }_{t=1}^{6}ESt\times Et\times IS\times EDL\times LS$$

(3)

where ESt is the ratio of carbon emissions of t chemical products to total emissions, indicating the product emission structure of the chemical industry; Et is the ratio of the total emissions of the chemical industry to the total output value of the new coal C chemical industry and represents the emission intensity of the chemical industry. IS is the ratio of chemical industrial output value to total industrial output value and indicates the industrial structure. EDL refers to the ratio of the total industrial output value to the average annual number of industrial employees and indicates the level of economic development. LS is the annual average number of industrial employees and indicates the size of the labor force. Through the above transformation, the influencing factors of the chemical industry’s carbon emissions were decomposed into product emission structure effect, emission intensity effect, industrial structure effect, economic development level effect, and labor force size effect.

According to the LMDt model summation method, the change in carbon emissions from the base period to the T period can be decomposed into the sum of the above effects:

$$\mathsf{\Delta }{C}_{total}=C^T-C^0=\mathsf{\Delta }{C}_{ES}+\mathsf{\Delta }{C}_{Et}+\mathsf{\Delta }{C}_{tS}+\mathsf{\Delta }{C}_{EDL}\mathsf{\Delta }{C}_{LS}$$

(4)

Among them,

$$\mathsf{\Delta }{C}_{ES}={\sum }_{t=1}^6\mathsf{\Delta }{C}_{ES}$$

In Equation (4),

$$\mathsf{\Delta }{C}_{EI}={\sum }_{t=1}^6\frac{C_t^T-C_t^o}{\ln C_t^T-\ln C_t^o}\ln \frac{E{I}^T}{E{I}^O}$$

(5)

$$\mathsf{\Delta }{C}_{IS}={\sum }_{t=1}^6\frac{C_t^T-C_t^o}{\ln C_t^T-\ln C_t^o}\ln \frac{I{S}^T}{I{S}^O}$$

(6)

$$\mathsf{\Delta }{C}_{LS}={\sum }_{t=1}^6\frac{C_t^T-C_t^o}{\ln C_t^T-\ln C_t^o}\ln \frac{L{S}^T}{L{S}^O}$$

(7)

$$\mathsf{\Delta }{C}_{EDL}={\displaystyle \sum }_{t=1}^6\frac{C_t^T-C_t^o}{\ln C_t^T-\ln C_t^o}\ln \frac{ED{L}^T}{ED{L}^O}$$

(8)

$$\mathsf{\Delta }{C}_{LS}={\displaystyle \sum }_{t=1}^6\frac{C_t^T-C_t^o}{\ln C_t^T-\ln C_t^o}\ln \frac{L{S}^T}{L{S}^O}$$

(9)

In Equations (4)–(9), ΔC_ES and D_ES represent the effect of product emission structure, ΔC_Et and D_Et represent the effect of carbon emission intensity, ΔC_tS and D_tS represent the effect of industrial structure, ΔC_EDL and D_EDL represent the effect of economic development level, and ΔC_LS and D_LS represent the effect of labor size.

2.2.3. Scenario Analysis of the Carbon Peak and Carbon Emission Reduction Paths of the Chemical Industry

The changes in carbon emissions in the chemical industry for 2021–2030 under different scenarios were predicted, and the paths of carbon emission reductions in the future of the chemical industry were discussed. The chemical industry development scenarios were divided into an existing policy scenario, a specific policy scenario, and a landing policy scenario. Policy classification was mainly based on the application and promotion rate of carbon capture, usage, and storage (CCUS) technology and the standard of new production capacity in the chemical industry. CCUS technology is of great significance for carbon emission reduction in coal-based enterprises. Therefore, the application and promotion rate of CCUS technology was used as the main adjusting factor in the scenario setting. China’s chemical industry has developed rapidly over the past decade, with a substantial expansion in production capacity. However, due to the influence of technology, cost, profit, and carbon emissions, the chemical industry has had a lower-than-expected capacity and an operating rate of less than 100% for a long time. In the context of China’s goal of achieving carbon neutrality by 2060, the chemical industry is facing the challenge of reducing its carbon emissions. Therefore, the new capacity of the chemical industry was used as another criterion for setting scenarios.

Table 1. Future development scenarios for the chemical industry.

Policy Scenario	CCUS Technology	Added Capacity
Existing policy scenario	No large-scale promotion or use of CCUS technology	During the 14th Five-Year Plan period, China approved the capacity expansion of projects under construction and existing projects. The chemical industry’s production capacity will not be increased during the 155-year period.
Specific policy scenario	Promotion of CCUS technology	During the 14th Five-Year Plan period, China approved the capacity expansion of projects under construction and existing projects. The chemical industry’s production capacity will not be increased during the 155-year period.
Landing policy scenario	Large-scale promotion of CCUS technology	During the 14th Five-Year Plan period, China approved the new capacity expansion of projects under construction and existing projects. The chemical industry’s production capacity will not be increased during the 155-year period.

Note: The CCUS CO₂ conversion rate is 49% (Wei Ning, 2021). CCUS—carbon capture, usage, and storage.

shows future development scenarios for the chemical industry.

3. Data Sources and Processing

3.1. Data Sources of Carbon Emission Measurement and Influencing Factors

As China has not yet carried out systematic statistics or disclosure of chemical industry-related data, the output data were obtained from the state’s official reports, listed chemical companies’ annual reports, the websites of major associations, and other public data sources to ensure data integrity and accuracy. The carbon emission coefficients of various chemical products were based on Zhang [10] and Ltu [11]. In addition, there was no specific classification of the chemical industry in the national economic classification, so the output values of the petroleum processing, coking (coal), nuclear fuel processing, chemical raw materials processing, and chemical products manufacturing industries were replaced by those of the chemical industry. The output value and total industrial output value of the chemical industry for 2010–2016 were obtained from the China Industrial Statistical Yearbook for 2011–2017, and the data for 2017–2019 were obtained from public information. The annual average number of industrial employees for 2010–2016 was obtained from China Industrial Statistical Yearbook, but data for 2012 were missing. SPSS was used to supplement this missing data and predict the annual average number of industrial employees for 2017–2019.

Table 2. Carbon emission coefficients of chemical industry products (tCO₂/t).

	Chemical Industrial Products
	Coal to Alcohol to Olefin	Alcohol to Olefin	Ethylene Glycol	Natural Gas	Direct Coal to Oil	Indirect Coal to Oil
Carbon emission coefficient	11.52	2.92	10.45	6.88	4.21	7.72
tCO2/t	Coal to Olefin	Alcohol to Olefin	Ethylene Glycol	Natural Gas	tCO2/t	Direct. Coal to Oil
Zhang (2019)	10.50	2.808	5.91	6.66	Zhang (2019)	4.02
Wang (2021)	11.52	2.92	10.45	6.88	Wang (2021)	4.21
Difference (%)	+9.7%	+3.98%	+76.8%	+3.3%	Difference (%)	+4.07%

shows the carbon emission coefficients of various chemical products [3].

Based on F_t factor importance in methodology and results and based on Zhang’s concern to provide new values (enterprises A-W) and also present previous ones, I think it would be of great importance to present all these values, the minimum (low bound), average (mean) and maximum values (upper bound). This statistic is important. It would be important to perform asensitivity analysis based on these mean values on the article results, principally in the ethylene glycolfield, which presented an increase of 76% in two years (Zhang data: 2019 and Wang data: 2021). The forecast value will also increase by 61% in 2024 (509–822 ten thousand tons).

3.2. Data Sources for Scenario Analysis

CCUS technology has not been widely employed in the chemical industry. Therefore, the carbon emission reduction intensity of the chemical industry should determine the application and promotion rate of CCUS technology in accordance with the situation of coal used

Table 3. CCUS promotion utilization rate in different scenarios.

Year	Existing Policy Scenario (%)	Specific Policy Scenario (%)	Landing Scenario (%)
2021	1.0	1.0	1.0
2022	1.0	5.0	10.0
2023	1.0	5.0	10.0
2024	1.0	10.0	30.0
2025	1.0	10.0	50.0
2026	1.0	30.0	70.0
2027	1.0	50.0	85.0
2028	1.0	70.0	90.0
2029	1.0	85.0	100.0
2030	1.0	100.0	100.0
2031	1.0	100.0	100.0

.

The capacity and output of the chemical industry in the scenario analysis were based on data from 2019. As the chemical industry was affected by the emergence of COVID-19 in 2020, the scenario was projected for 2021–2030. The main consideration for new capacity in the chemical industry was the smooth operation of demonstration projects during the 14th Five-year Plan period, during which the construction and operation of approved projects will be steadily promoted, and new projects will take three or four years to reach production. Additionally, due to the difficulty in fully determining the capacity of the chemical industry, the capacity utilization rate and the actual output of chemical projects in 2021–2030 were predicted based on the principle of increasing the effective capacity utilization rate each year.

Table 4. Capacity and output of the chemical industry.

	Alcohol to Olefin (Ten Thousand Tons)	Glycol (Ten Thousand Tons)	Natural Gas (Billion m3)	Direct Coal to Oil (Ten Thousand Tons)	Indirect Coal to Oil (Ten Thousand Tons)
Annual capacity	1675	509	54.6	122	832
Annual output	1273.8	324.5	49.8	91	679.2
Capacity utilization	76%	64%	91.2%	75%	82%

and

Table 5. Capacity forecast of the chemical industry.

Year	Alcohol to Olefin (Ten Thousand Tons)	Glycol (Ten Thousand Tons)	Natural Gas (Billion m3)	Direct Coal to Oil (Ten Thousand Tons)	Indirect Coal to Oil (Ten Thousand Tons)
2021	1675	509	54.6	122	832
2022	1675	509	54.6	122	832
2023	1675	509	54.6	122	832
2024	1746	822	73.7	122	832
2025	1746	822	73.7	122	921
2026	1746	822	73.7	122	921
2027	1746	822	73.7	122	921
2028	1746	822	73.7	122	921
2029	1746	822	73.7	122	1345
2030	1746	822	73.7	122	1345

show the capacity and output of the chemical industry in 2019 and the capacity forecast of the chemical industry in 2021–2030, respectively [4].

4. Results and Analysis

The results of carbon emissions from the chemical industry for 2010–2019 cover the 12th and 13th Five-Year Plans of China for a total of 10 years of implementation. Over the past ten years, China’s economy has experienced high-speed growth, a transition from high-speed growth to medium-high growth, and then medium-high growth. Under the background of the country’s economic development, China’s chemical industry has undergone many transformations and upgrades. In the last decade, the chemical industry developed rapidly, and its production capacity expanded rapidly. The output of chemical products increased from 147,000 tons in 2010 to 26,479,200 tons in 2019, with an average annual growth rate of 78.08%. During this period, carbon emissions also increased each year.

Table 6. Carbon emissions of chemical products.

	Olefin Tons	Glycol Tons	Natural Gas Tons	Direct Coal to Oil Tons	Indirect Coal to Oil Tons	Olefin Tons	All Combined Tons
2010	0	0	0	0	0	1.12	1.12
2011	8.15	0	0	0	0	1.61	9.76
2012	8.15	0	1.37	0	3.51	1.94	14.97
2013	14.62	1.19	3.16	0.2	3.55	2.19	24.91
2014	19.22	1.62	6.12	3.73	3.48	2.18	36.35
2015	36.45	8.65	7.79	7.56	3.52	2.35	66.32
2016	29.55	6.99	10.53	10.23	3.55	8.56	69.41
2017	35.56	8.46	16.22	12.51	3.58	18.77	95.1
2018	60.98	14.44	25.56	14.23	3.66	41.35	160.22
2019	71.82	16.99	33.23	20.39	3.67	49.98	196.08
All combined	284.5	58.34	103.98	68.85	28.52	130.05	198.89

shows that carbon emissions increased from 1.13 million tons in 2010 to 196.32 million tons in 2019, with an average annual growth of 77.37% [12].

The carbon emissions trends for different chemical products in 2010–2019. The carbon emissions from coal to Alcohol to Olefin had the fastest growth in this decade, followed by indirect coal to oil. The growth rate was based on both the output growth and the carbon emission coefficient. The growth rates of carbon emissions from coal to ethylene glycol, coal to natural gas, and methanol to Alcohol to Olefin were shorter than those from coal to Alcohol to Olefin and coal to oil. It follows that coal to Alcohol to Olefin and coal to oil were the main indirect contributors to the increases in carbon emissions in the chemical industry [13].

4.1. Overall Analysis of Decomposition Results

Factors influencing carbon emissions in the chemical industry were decomposed by the LMDt model, and

Table 7. Decomposition results of chemical industry carbon emission factors.

Year	Discharge Structure		Emission Intensity		Industry Structure		Level of Economic Development		Labor Force Dynamics		Overall Effect
	ΔCES	${\mathit{D}}_{\mathit{E}\mathit{S}}$	ΔCEI	${\mathit{D}}_{\mathit{E}\mathit{I}}$	ΔCES	${\mathit{D}}_{\mathit{E}\mathit{S}}$	ΔCEDL	${\mathit{D}}_{\mathit{E}\mathit{D}\mathit{L}}$	ΔCLS	${\mathit{D}}_{\mathit{L}\mathit{S}}$	ΔCtotal	Dtotal
2011	−2.26	0.56	7.65	6.76	0.27	1.06	0.82	1.24	−0.15	0.95	6.26	4.85
2012	−2.28	0.58	12.02	9.64	0.36	1.06	1.33	1.28	−0.04	0.98	10.92	7.79
2013	−3.42	0.64	20.33	14.52	0.73	1.11	2.17	1.31	0.21	1.05	20.02	13.88
2014	−3.88	0.68	30.36	20.22	0.98	1.11	3.29	1.39	0.46	1.06	31.32	21.98
2015	−5.01	0.73	59.62	39.25	0.62	1.05	5.85	1.42	0.38	1.01	61.33	43.92
2016	−6.52	0.68	60.62	39.21	0.27	1.01	7.29	1.53	−0.13	0.98	61.59	41.39
2017	−8.86	0.66	83.85	50.88	−1.05	0.96	11.17	1.69	0.37	1.03	84.49	55.39
2018	−12.88	0.65	145.86	92.49	−7.02	0.81	19.26	1.82	1.32	1.03	145.62	94.68
2019	−14.92	0.65	180.91	117.68	−11.6	0.72	24.06	1.88	2.46	1.06	180.26	117.36
Cumulative effect	−60.3		601.22		−16.52		75.24		4.88		601.81
Contribution rate (%)	−10.01		99.88		−2.74		12.5		0.81		100.01

shows the results. Overall, the carbon emissions of China’s chemical industry increased over time. Among the influencing factors, the emission intensity effect had the biggest pulling effect on carbon emissions, followed by the economic development level. The size of the workforce had a small contribution to carbon emissions. The structure effect of emissions was the main factor in mitigating the growth of carbon emissions (always <1). Additionally, the industrial structure effect showed a promotion effect as it promoted the growth of carbon emissions in 2010–2016 and mitigated the growth of carbon emissions in 2016–2019.

4.2. Specific Analysis of Decomposition Results

(1)

Emission structure effect: Emission structure refers to the ratio of carbon emissions of different chemical products to total carbon emissions. According to the decomposition results, the emission structure effect was the most important factor inhibiting carbon emissions, and its inhibitory effect increased in strength from −2.27 in 2011 to −14.91 in 2019. This was mainly due to the different output and carbon emission factors of different chemical products; it also indicates that improving the emission structure of the chemical industry can effectively mitigate the growth of carbon emissions in the chemical industry through means such as decelerating the capacity construction of coal-to-Alcohol to Olefin projects [14].

(2)

Emission intensity effect: Carbon emission intensity represents the ratio of carbon emissions to the industrial output value, which can reflect the level of carbon emissions per unit output value of chemical products. The decomposition results show that the emission intensity effect was the most powerful factor promoting carbon emissions in the chemical industry; this value reached 180.38 in 2019, mainly because the growth rate of carbon emissions in the chemical industry was much faster than that of the output value, resulting in increased emission intensity [15].

(3)

Industrial structure effect: Industrial structure refers to the proportion of chemical industrial output value to the total industrial output value. Table 7 shows that the industrial structure effect promoted carbon emissions in the chemical industry, mainly because the rapid development of the chemical industry is causing the proportion of the total industrial output value to rise.

(4)

Level of economic development effect: The decomposition results show that the level of economic development is one of the main factors driving carbon emissions in the chemical industry, from 5.84 in 2010 to 24.07 in 2019. This is mainly because economic development has created more markets and demands, driving the rapid development of the chemical industry, and carbon emissions have increased. In the foreseeable future, the level of economic development will continue to be the main factor promoting the growth of carbon emissions from the chemical industry [16].

(5)

Labor force size effect: Table 7 shows that the labor force size effect had a weak role in promoting carbon emissions in the chemical industry. This shows that the scale of the labor force played an important role in the development of the chemical industry. The chemical industry now needs a high-quality, highly educated, and skilled labor force to meet the requirements of green development, energy conservation, and emissions reductions [17].

4.3. Analysis of Carbon Emission Reduction Paths and Carbon Peak Scenarios for the Chemical Industry

Evidently, under the existing policy scenario, carbon emissions will increase rapidly with the expansion of production capacity and the improvement of production efficiency due to the lack of large-scale use of CCUS technology. The chemical industry will reach its carbon peak of 356.96 million tons in 2028. In the specific policy scenario, due to the promotion and application of CCUS technology, carbon emissions from the chemical industry will peak at 30.8 million tons in 2025, after which they will begin to decline rapidly. Additionally, with the chemical industry productivity efficiency and CCUS technology promotion and utilization rate reaching 100%, the chemical industry’s carbon emissions will reach the lowest value of 182.94 million tons in 2029. In the landing policy scenario, due to the large-scale promotion and application of CCUS technology, the chemical industry will have a significant rate of carbon emissions reduction, reaching its peak of 257.81 million tons in 2025, which is smaller than the peak of carbon emissions in the specific policy scenario. Under this enhanced scenario, the carbon emissions in the chemical industry will reach a stable value of 182.94 million tons in 2028 [18]

Table 8. CCUS promotion utilization rate in different scenarios.

Year	Existing Policy Scenario Carbon Emissions (Million Tons)	Specific Policy Scenario Carbon Emissions (Million Tons)	Landing Scenario Carbon Emissions (Million Tons)
2021	209.32	209.66	209.96
2022	212.12	207.82	191.28
2023	238.36	236.93	218.87
2024	258.96	233.55	196.76
2025	338.43	309.54	256.99
2026	348.82	265.65	212.76
2027	355.76	268.98	182.76
2028	355.76	218.12	186.23
2029	359.98	185.96	186.23
2030	361.22	185.96	186.23

.

The chemical industry reaches a carbon peak before 2030 under all three scenarios; it will reach the peak later under the existing policy scenario than in the specific policy scenario and the landing policy scenario, and the peak value will be much higher than those in the latter two scenarios. Carbon emissions will peak in the same year under the specific and landing policy scenarios, but the peak value under a specific policy scenario will be higher than that under landing policy scenarios. From the perspective of carbon reduction potential, carbon emissions under the specific and landing policy scenarios will be 49% lower in 2030 than those under established policy scenarios. From the perspective of an emissions reduction path, the emission reduction path under the landing policy scenario is the most beneficial for the chemical industry to achieve carbon neutrality as soon as possible. Under this path, the chemical industry will reach its carbon peak in 2025, and carbon emissions will stabilize in 2028. After that, carbon emissions will be further reduced, and the emissions reduction rate will be further improved through improving CCUS technology and process flows. With the improvement of forest carbon sequestration mechanisms, the chemical industry will rapidly achieve carbon neutrality [12].

5. Conclusions and Countermeasures

In this study, the carbon emissions of China’s chemical industry for 2010–2019 were estimated, and then the LMDt model was utilized to study the factors influencing carbon emissions in China’s chemical industry. Finally, different scenarios were created to predict the carbon emissions of China’s chemical industry for 2021–2030 and the following conclusions were drawn. See Ft factor to increase the results and discussion improvement’s.

First, the carbon emissions of China’s chemical industry showed rapid growth in 2010–2019. Carbon emissions from the chemical industry reached 196 million tons in 2019, accounting for 2% of the country’s total carbon emissions. In the context of achieving carbon neutrality, the task of reducing emissions in the chemical industry is arduous. Second, the emission structure effect was found to be the most important factor for mitigating the growth of carbon emissions in China’s chemical industry, which indicates that improving the chemical industry’s emissions structure can effectively mitigate the growth of carbon emissions [13]. Third, the scenario prediction results show that the chemical industry will reach a carbon peak before 2030 under the existing, specific, and landing policy scenarios. Among them, the existing policy scenario will reach its peak in 2028 at the latest. The specific and landing policy scenarios will both reach their peaks in 2025, and the carbon emissions of the chemical industry will stabilize at 182.94 million tons before 2030. After that, due to improvements in process technology and CCUS technology, the chemical industry’s carbon emissions will decrease each year. Under the strong implementation of China’s continuous increase in forest carbon sequestration and other emission reduction measures, the chemical industry will achieve the country’s goal of carbon neutrality by 2060 [19].

The carbon emission intensity of China’s chemical industry rose continually in 2010–2019, so it is necessary to vigorously develop clean chemical technology, improve the process level, and reduce various processes’ carbon emissions. Additionally, China should actively extend the chemical industry chain, improve the downstream industry construction of the chemical industry chain, and increase the output value of the chemical industry chain [20].

In recent frontier exploratory research on CO₂ resource utilization, photocatalytic CO₂ conversion and generation of synthetic gas and fuel (methane, methanol, ethanol, liquid) under normal temperature and pressure have been accomplished, and a simulated photosynthetic reaction between CO₂ and water catalyzed by enzyme-state hydrocarbon and chemicals (formic acid, ethylene, acetic acid, ethylene glycol, etc.) has also been accomplished.

Recently, the Tianjin Institute of Industrial Biotechnology at the Chinese Academy of Sciences used CO₂ as a raw material—independent of plant photosynthesis—in the laboratory. The institute achieved total synthesis from CO₂ to starch molecules for the first time using biological enzymes.

In the long term, if humanity uses little or no fossil fuels in the future, we will still need carbon-based fuels, carbon-based chemistry products, and carbon-based materials to sustain social development; collected high-concentration CO₂ raw materials can be used for CO₂ resource utilization. The good news is the recent emergence of direct air capture (DAC) technology can capture and produce high concentrations of CO₂ from air with a very low concentration of CO₂. If DAC technology can produce high-concentration CO₂ as raw material, combined with the above CO₂ resource utilization technology and green ammonia synthesis technology, humans can create a CO₂ chemical system with zero carbon emissions (Figure 1). As shown in Figure 1, CO₂ can be captured in air.

As raw materials, green electricity and green hydrogen are used to synthesize carbon-based fuels, carbon-based chemicals, carbon-based materials, fertilizers, and even starch for human needs. These products are re-discharged into the air with CO₂ and water after consumption, use, and degradation treatment. The whole system forms a closed-loop carbon cycle that maintains a balance of CO₂ and thus has zero carbon emissions [20]. This envisaged system may be the ultimate technological solution to divest from fossil fuels and achieve carbon neutrality. The scheme would make it possible for everything to be powered by solar energy (sunlight, photovoltaic, and wind power), air (CO₂ and N₂), and water as the necessary fuels, as well as chemicals and materials for the large-scale production of raw materials [21].

In the frontier exploratory research of CO₂ resource utilization, in recent years, photocatalytic CO₂ conversion and generation under normal temperature and pressure Synthetic gas, fuel (methane, methanol, ethanol, liquid) can be produced by CO₂ conversion and simulated photosynthetic reaction between CO₂ and water catalyzed by enzyme State hydrocarbon, etc.) and chemicals (formic acid, ethylene, acetic acid, ethylene glycol, etc.) have made important research progress; especially, Tianjin Institute of Industrial Biotechnology of Chinese Academy of Sciences took CO₂ as raw material, independent of plant photosynthesis in the laboratory, total synthesis from CO₂ to starch molecules was achieved for the first time using biological enzyme method.

A solar air-fuel system consisting of three basic cells has recently been tested at ETH Zurich Yuan. The cells consist of a DAC CO₂ extraction unit, a solar electricity reduction of CO₂ and water into CO and H₂ syngas unit, and a syngas catalytic synthesis of liquid hydrocarbon or methanol unit.